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Status: Unchecked ×Keyword: sequential model-based optimization ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Progressive Neural Architecture Search

   Liu, Zoph, Neumann et al. · arXiv (Cornell University) · 2017

   The paper proposes a more efficient way to search for convolutional neural network structures, using sequential model-based optimisation, and reports state-of-the-art accuracies on CIFAR-10 and ImageNet.

   Unchecked2 claims
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   1. UncheckedUnder the same search space, the authors' method needs up to 5 times fewer models evaluated and 8 times less compute than Zoph et al.'s (2018) RL method.“Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute.”
   2. UncheckedNetwork structures found by the authors' search method reach state-of-the-art classification accuracy on the CIFAR-10 and ImageNet image datasets.“The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.”